Methods › Computer Vision › Vision Transformers › EsViT
EsViT
Introduced by Chunyuan Li et al. in Efficient Self-supervised Vision Transformers for Representation Learning
archive 2025-07-28 Description, source and code snippet are the archive's method entry.
EsViT proposes two techniques for developing efficient self-supervised vision transformers for visual representation leaning: a multi-stage architecture with sparse self-attention and a new pre-training task of region matching. The multi-stage architecture reduces modeling complexity but with a cost of losing the ability to capture fine-grained correspondences between image regions. The new pretraining task allows the model to capture fine-grained region dependencies and as a result significantly improves the quality of the learned vision representations.
Papers archive 2025-07-28
1 shown of 1, newest first. Repository counts are the archive's code-links table. A Syntology line states what Syntology ran from that paper's harvested code; it is per sample and not a correctness claim.
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Efficient Self-supervised Vision Transformers for Representation Learning 17 Jun 2021 · 1 repository · arXiv:2106.09785Syntology ran 2 of 6 samples · 4 unverified
Tasks archive 2025-07-28
2 tasks the archive attaches to papers tagged with this method, by distinct papers. A task without a page in the catalog is plain text.
Usage over time archive 2025-07-28
Components: the archive holds no method-to-method composition, so PwC's Components table cannot be rebuilt; the Papers list carries no Results column for the same reason (the archive does not join its leaderboard rows to method tags).
Categories archive 2025-07-28
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